Statistics from geopandas data

Calculate building statistics from a geopandas GeoDataFrame.

import geopandas as gpd
import matplotlib.pyplot as plt
import seaborn as sns
from cityseer.metrics import layers
from cityseer.tools import graphs, io

To start, follow the same approach as shown in the network examples to create the network.

streets_gpd = gpd.read_file("data/madrid_streets/street_network.gpkg")
streets_gpd = streets_gpd.explode(reset_index=True)
G = io.nx_from_generic_geopandas(streets_gpd)
G = graphs.nx_decompose(G, 50)
G_dual = graphs.nx_to_dual(G)
nodes_gdf, _edges_gdf, network_structure = io.network_structure_from_nx(G_dual)

INFO:cityseer.tools.graphs:Merging parallel edges within buffer of 1.
INFO:cityseer.tools.graphs:Decomposing graph to maximum edge lengths of 50.
INFO:cityseer.tools.graphs:Converting graph to dual.
INFO:cityseer.tools.graphs:Preparing dual nodes
INFO:cityseer.tools.graphs:Preparing dual edges (splitting and welding geoms)
INFO:cityseer.tools.io:Preparing node and edge arrays from networkX graph.
INFO:cityseer.graph:Edge R-tree built successfully with 195313 items.

Read-in the dataset from the source Geopackage or Shapefile Geopandas.

bldgs_gpd = gpd.read_file("data/madrid_buildings/madrid_bldgs.gpkg")
bldgs_gpd.head()

	mean_height	area	perimeter	compactness	orientation	volume	floor_area_ratio	form_factor	corners	shape_index	fractal_dimension	geometry
0	NaN	187.418714	58.669276	0.491102	40.235999	NaN	NaN	NaN	4	0.700787	1.026350	POLYGON ((448688.642 4492911, 448678.351 44928...
1	7.0	39.082821	26.992208	0.472874	10.252128	273.579749	78.165643	5.410857	4	0.687658	1.041691	POLYGON ((440862.665 4482604.017, 440862.64 44...
2	7.0	39.373412	27.050303	0.475086	10.252128	275.613883	78.746824	5.400665	4	0.689265	1.040760	POLYGON ((440862.681 4482608.269, 440862.665 4...
3	7.5	37.933979	26.739878	0.464266	10.252129	284.504846	75.867959	5.513124	4	0.681371	1.045072	POLYGON ((440862.705 4482612.365, 440862.681 4...
4	7.0	39.013701	26.972641	0.472468	10.183618	273.095907	78.027402	5.412350	4	0.687363	1.041798	POLYGON ((440880.29 4482607.963, 440880.274 44...

Use the layers.compute_stats method to compute statistics for numeric columns in the GeoDataFrame. These are specified with the stats_column_labels argument. These statistics are computed over the network using network distances. In the case of weighted variances, the contribution of any particular point is weighted by the distance from the point of measure.

distances = [100, 200]
nodes_gdf, bldgs_gpd = layers.compute_stats(
    bldgs_gpd,
    stats_column_labels=[
        "area",
        "perimeter",
        "compactness",
        "orientation",
        "shape_index",
    ],
    nodes_gdf=nodes_gdf,
    network_structure=network_structure,
    distances=distances,
)

INFO:cityseer.metrics.layers:Computing statistics.
INFO:cityseer.metrics.layers:Assigning data to network.
INFO:cityseer.data:Assigning 135302 data entries to network nodes (max_dist: 100).
INFO:cityseer.data:Collected 1282627 potential node assignments from data entries.
INFO:cityseer.data:Finished assigning data. 1282627 assignments added to 89639 nodes.
INFO:cityseer.graph:Barriers unset and R-tree cleared.
INFO:cityseer.config:Metrics computed for:
INFO:cityseer.config:Distance: 100m, Beta: 0.04, Walking Time: 1.25 minutes.
INFO:cityseer.config:Distance: 200m, Beta: 0.02, Walking Time: 2.5 minutes.

This will generate a set of columns containing count, sum, min, max, mean, and var, in unweighted nw and weighted wt versions (where applicable) for each of the input distance thresholds.

nodes_gdf.columns

Index(['ns_node_idx', 'x', 'y', 'live', 'weight', 'primal_edge',
       'primal_edge_node_a', 'primal_edge_node_b', 'primal_edge_idx',
       'dual_node',
       ...
       'cc_shape_index_sum_200_nw', 'cc_shape_index_sum_200_wt',
       'cc_shape_index_mean_200_nw', 'cc_shape_index_mean_200_wt',
       'cc_shape_index_count_200_nw', 'cc_shape_index_count_200_wt',
       'cc_shape_index_var_200_nw', 'cc_shape_index_var_200_wt',
       'cc_shape_index_max_200', 'cc_shape_index_min_200'],
      dtype='object', length=110)

The result in columns can be explored with conventional Python ecosystem tools such as seaborn and matplotlib.

sns.histplot(
    data=nodes_gdf,
    x="cc_orientation_mean_200_wt",
    bins=50,
)

fig, ax = plt.subplots(1, 1, figsize=(8, 8), facecolor="#1d1d1d")
nodes_gdf.plot(
    column="cc_orientation_mean_200_wt",
    cmap="Dark2",
    legend=False,
    vmin=0,
    vmax=45,
    ax=ax,
)
bldgs_gpd.plot(
    column="orientation",
    cmap="Dark2",
    legend=False,
    vmin=0,
    vmax=45,
    alpha=0.5,
    ax=ax,
)
ax.set_xlim(438500, 438500 + 3500)
ax.set_ylim(4472500, 4472500 + 3500)
ax.axis(False)

(np.float64(438500.0),
 np.float64(442000.0),
 np.float64(4472500.0),
 np.float64(4476000.0))